OpenCV & Python: quickly superimpose mask over image without overflow -


i'd superimpose binary mask on color image, such mask "on", pixel value changes amount can set. result should this:

enter image description here

i using opencv 2.4 , python 2.7.6. have way works well, slow, , way fast has issues overflow , underflow. here result of faster code, overflow/underflow artifacts:

enter image description here

here code, showing both fast version , slow version:

def superimpose_mask_on_image(mask, image, color_delta = [20, -20, -20], slow = false):     # superimpose mask on image, color change being controlled color_delta     # todo: works on 3-channel, 8 bit images , 1-channel, 8 bit masks      # fast, can't handle overflows     if not slow:         image[:,:,0] = image[:,:,0] + color_delta[0] * (mask[:,:,0] / 255)         image[:,:,1] = image[:,:,1] + color_delta[1] * (mask[:,:,0] / 255)         image[:,:,2] = image[:,:,2] + color_delta[2] * (mask[:,:,0] / 255)      # slower, no issues overflows     else:         rows, cols = image.shape[:2]         row in xrange(rows):             col in xrange(cols):                 if mask[row, col, 0] > 0:                     image[row, col, 0] = min(255, max(0, image[row, col, 0] + color_delta[0]))                     image[row, col, 1] = min(255, max(0, image[row, col, 1] + color_delta[1]))                     image[row, col, 2] = min(255, max(0, image[row, col, 2] + color_delta[2]))      return

is there fast way (probably using of numpy's functions) same result slow code produces?

there might better ways of applying colorizing mask image, if want way suggest, simple clipping want:

import numpy np  image[:, :, 0] = np.clip(image[:, :, 0] + color_delta[0] * (mask[:, :, 0] / 255), 0, 255) image[:, :, 1] = np.clip(image[:, :, 1] + color_delta[1] * (mask[:, :, 0] / 255), 0, 255) image[:, :, 2] = np.clip(image[:, :, 2] + color_delta[2] * (mask[:, :, 0] / 255), 0, 255)

the result is:

enter image description here

another way modify hue/saturation if goal apply color region. instance:

mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.bool) mask[100:200, 100:500] = true  image = cv2.cvtcolor(image, cv2.color_bgr2hsv) image[mask, 0] = 80 image[mask, 1] = 255 image = cv2.cvtcolor(image, cv2.color_hsv2bgr)

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